scholarly journals Towards an Effective Imaging-Based Decision Support System for Skin Cancer

2022 ◽  
pp. 354-382
Author(s):  
Ricardo Vardasca ◽  
Carolina Magalhaes
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Skin Cancer ◽  
Medical Imaging ◽  
Decision Support System ◽  
Support System ◽  
Imaging Modality ◽  
Imaging Modalities ◽  
Background Information ◽  
Available Technology

The usage of expert systems to aid in medical decisions has been employed since 1980s in distinct applications. With the high demands of medical care and limited human resources, these technologies are required more than ever. Skin cancer has been one of the pathologies with higher growth, which suffers from lack of dermatology experts in most of the affected geographical areas. A permanent record of examination that can be further analyzed are medical imaging modalities. Most of these modalities were also assessed along with machine learning classification methods. It is the aim of this research to provide background information about skin cancer types, medical imaging modalities, data mining and machine learning methods, and their application on skin cancer imaging, as well as the disclosure of a proposal of a multi-imaging modality decision support system for skin cancer diagnosis and treatment assessment based in the most recent available technology. This is expected to be a reference for further implementation of imaging-based clinical support systems.

scholarly journals Decision Support System for Stock Portfolio Selection Using Artificial Intelligence and Machine Learning

2021 ◽  
Vol 26 (1) ◽  
pp. 87-93
Author(s):  
Sandeep Patalay ◽  
Madhusudhan Rao Bandlamudi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Support ◽  
Stock Market ◽  
Decision Support System ◽  
Portfolio Selection ◽  
Support System ◽  
Financial Analysis ◽  
Traditional Approach ◽  
Stock Portfolio

Investing in stock market requires in-depth knowledge of finance and stock market dynamics. Stock Portfolio Selection and management involve complex financial analysis and decision making policies. An Individual investor seeking to invest in stock portfolio is need of a support system which can guide him to create a portfolio of stocks based on sound financial analysis. In this paper the authors designed a Financial Decision Support System (DSS) for creating and managing a portfolio of stock which is based on Artificial Intelligence (AI) and Machine learning (ML) and combining the traditional approach of mathematical models. We believe this a unique approach to perform stock portfolio, the results of this study are quite encouraging as the stock portfolios created by the DSS are based on strong financial health indices which in turn are giving Return on Investment (ROI) in the range of more than 11% in the short term and more than 61% in the long term, therefore beating the market index by a factor of 15%. This system has the potential to help millions of Individual Investors who can make their financial decisions on stocks and may eventually contribute to a more efficient financial system.

Motion Monitoring and Decision Support During Heavy Transport

2010 ◽  
Author(s):  
Onno A. J. Peters ◽  
Leon J. M. Adegeest
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Bending Moment ◽  
Background Information ◽  
Weather Forecasts ◽  
Measurement Systems ◽  
System A ◽  
Vessel Response ◽  
Transport Vessel

During transports of large heavy cargo like jack-up rigs or semi-submersibles, the Motion Monitoring and Captain Decision Support system is a valuable tool to ensure a safe and economical voyage. Using the dynamic characteristics of the vessel in combination with 5-day weather forecasts and design limits like maximum accelerations at the cargo location, roll motion and/or leg bending moment, more and better information is available to the Master to choose a safe heading, speed and route. This way the best knowledge what to expect is contributing to the safety of cargo, transport vessel and crew. Besides use in heavy transport, this system is widely used on container ships, LNG carriers, all kinds of offshore vessels and many other types of floating structures. With daily communication, all important information is made available on internet to the operator’s main office, from which clients are informed with a comprehensive and concise overview of what is happening with their property. After the voyage, clients can be provided with the recorded Motion Monitoring data, which is valuable information for the lifetime assessment. The paper is presenting background information of the Motion Monitoring and Captain Decision Support system, a brief overview of methods used by the system and is describing the relations between transport vessel, main office and client and between the Transport Manual and the system. Results of two independent measurement systems are giving proof of high accuracy of the measurements. Comparison between measurements and predicted vessel response are shown and explained.

Variables Influencing Machine Learning-Based Cardiac Decision Support System: A Systematic Literature Review

2019 ◽  
Vol 892 ◽  
pp. 274-283
Author(s):  
Mohammed Ashikur Rahman ◽  
Afidalina Tumian
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision ◽  
Machine Learning Techniques ◽  
Cardiac Care ◽  
Learning Techniques

Now a day, clinical decision support systems (CDSS) are widely used in the cardiac care due to the complexity of the cardiac disease. The objective of this systematic literature review (SLR) is to identify the most common variables and machine learning techniques used to build machine learning-based clinical decision support system for cardiac care. This SLR adopts the Preferred Reporting Item for Systematic Review and Meta-Analysis (PRISMA) format. Out of 530 papers, only 21 papers met the inclusion criteria. Amongst the 22 most common variables are age, gender, heart rate, respiration rate, systolic blood pressure and medical information variables. In addition, our results have shown that Simplified Acute Physiology Score (SAPS), Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE) are some of the most common assessment scales used in CDSS for cardiac care. Logistic regression and support vector machine are the most common machine learning techniques applied in CDSS to predict mortality and other cardiac diseases like sepsis, cardiac arrest, heart failure and septic shock. These variables and assessment tools can be used to build a machine learning-based CDSS.

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